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  Supervised learning sets benchmark for robust spike rate inference from calcium imaging signals

Bethge, M., Theis, L., Berens, P., Froudarakis, E., Reimer, J., Roman-Roson, M., et al. (2016). Supervised learning sets benchmark for robust spike rate inference from calcium imaging signals. Poster presented at Computational and Systems Neuroscience Meeting (COSYNE 2016), Salt Lake City, UT, USA.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0000-7BD1-A Version Permalink: http://hdl.handle.net/21.11116/0000-0001-18E4-3
Genre: Poster

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 Creators:
Bethge, M1, Author              
Theis, L1, Author              
Berens, P1, Author              
Froudarakis, E, Author
Reimer, J, Author
Roman-Roson, M, Author
Baden, T, Author
Euler, T, Author
Tolias, A, Author              
Affiliations:
1University of Tübingen, Werner Reichardt Centre of Integrative Neuroscience , ou_persistent22              

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 Abstract: A fundamental challenge in calcium imaging has been to infer spike rates of neurons from the measured noisy calcium fluorescence traces. We collected a large benchmark dataset (>100.000 spikes, 73 neurons) recorded from varying neural tissue (V1 and retina) using different calcium indicators (OGB-1 and GCaMP6s). We introduce a new algorithm based on supervised learning in flexible probabilistic models and systematically compare it against a range of spike inference algorithms published previously. We show that our new supervised algorithm outperforms all previously published techniques. Importantly, it even performs better than other algorithms when applied to entirely new datasets for which no simultaneously recorded data is available. Future data acquired in new experimental conditions can easily be used to further improve its spike prediction accuracy and generalization performance. Finally, we show that comparing algorithms on artificial data is not informative about performance on real data, suggesting that benchmark datasets such as the one we provide may greatly facilitate future algorithmic developments.

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 Dates: 2016-02
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: BethgeTBFRRBET2016
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Title: Computational and Systems Neuroscience Meeting (COSYNE 2016)
Place of Event: Salt Lake City, UT, USA
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Title: Computational and Systems Neuroscience Meeting (COSYNE 2016)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: II-98 Start / End Page: 163 - 163 Identifier: -